This document provides an introduction and overview of SmartPLS software for structural equation modeling. It discusses key concepts in SEM like latent and manifest variables, reflective and formative measurement models, and the structural and measurement models. It then demonstrates how to use SmartPLS by opening a project file, evaluating a sample research model and hypotheses, and interpreting the output metrics to assess model fit and quality.
Introduction to Structural Equation Modeling Partial Least Sqaures (SEM-PLS)Ali Asgari
Partial least squares structural equation modelling (PLS-SEM) has recently received considerable attention in a variety of disciplines.The goal of PLS-SEM is the explanation of variances (prediction-oriented approach of the methodology) rather than explaining covariances (theory testing via covariance-based SEM).
Introduction to SEM (Structural Equation Models) - invited talk at the seminar "Analyzing and Interpreting Data" organized by the Finnish Doctoral Programme in Education and Learning (15 May 2013) in Vuosaari, Helsinki, Finland. Acknowledgements to Barbara Byrne for an excellent intro book of SEM.
Introduction to Structural Equation Modeling Partial Least Sqaures (SEM-PLS)Ali Asgari
Partial least squares structural equation modelling (PLS-SEM) has recently received considerable attention in a variety of disciplines.The goal of PLS-SEM is the explanation of variances (prediction-oriented approach of the methodology) rather than explaining covariances (theory testing via covariance-based SEM).
Introduction to SEM (Structural Equation Models) - invited talk at the seminar "Analyzing and Interpreting Data" organized by the Finnish Doctoral Programme in Education and Learning (15 May 2013) in Vuosaari, Helsinki, Finland. Acknowledgements to Barbara Byrne for an excellent intro book of SEM.
What is path analysis?
What are general assumptions?
What is input path diagram?
What is output path diagram?
How unexplained variance is shown in path diagram?
Statswork- Lecture:1: Structural Equation Modeling (SEM) using AMOS (www.stat...Stats Statswork
Statswork (Statswork.com), Lecture:1 – Introductory Videos of Structural Equation Modeling (SEM) using AMOS, SEM and what are the values extracted from the Test?
Structural Equation Modeling (SEM) is an extension of the general linear model. It is used to test a set of regression equations simultaneously. SEM represents the relationship between dependent (unobserved) variable and independent (observed) variables using path diagrams. Watch more.
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
Estimators for structural equation models of Likert scale dataNick Stauner
Which estimation method is optimal for structural equation modeling (SEM) of Likert scale data? Conventional SEM assumes continuous measurement, and some SEM estimators assume a multivariate normal distribution, but Likert scale data are ordinal and do not necessarily resemble a discretized normal distribution. When treated as continuous, these data may yet be skewed due to item difficulty, choice of population, or various response biases. One can fit an SEM to a matrix of polychoric correlations, which estimate latent, continuous constructs underlying ordinally measured variables, but polychoric correlations also assume these latent factors are normally distributed. To what extent are these methods robust with continuous versus ordinal data and with varying degrees of skewness and kurtosis? To answer, I simulated 10,000 samples of multivariate normal data, each consisting of 500 observations of five strongly correlated variables. I transformed each consecutive sample to an incrementally greater degree to increase skew and kurtosis from approximately normal levels to extremes beyond six and 30, respectively. I then performed five confirmatory factor analyses on each sample using five different estimators: maximum likelihood (ML), weighted least squares (WLS), diagonally weighted least squares (DWLS), unweighted least squares (ULS), and generalized least squares (GLS). I compared results for continuous and discretized (ordinal) data, including loadings, error variances, fit statistics, and standard errors. I also noted frequencies of failures, which complicated calculation of polychoric correlations, and particularly plagued the WLS estimator. WLS estimation produced relatively biased loadings and error variance estimates. GLS also underestimated error variances. Neither estimator exhibited any unique advantage to offset these disadvantages. ML estimated parameters more accurately, but some fit statistics appeared biased by it, especially in the context of extreme nonnormality. Specifically, the chi squared goodness-of-fit test statistic and the root mean square error of approximation (RMSEA) began higher with ML-estimated SEMs of approximately normal data, and worsened sharply with greater nonnormality. The Tucker Lewis Index (TLI) and standardized root mean square residual (SRMR) also worsened more moderately with nonnormality when using ML estimation. GLS-estimated fit statistics shared ML’s sensitivity to nonnormality, and were even worse for the TLI and SRMR. Results generally favored ULS and DWLS estimators, which produced accurate parameter estimates, good and robust fit statistics, and small standard errors (SEs) for loadings. DWLS tended to produce smaller SEs than ULS when skewness was below three, but ULS SEs were more robust to nonnormality and smaller with extremely nonnormal data. ML SEs were larger for loadings, but smaller for error variance estimates, and fairly robust to nonnormality...
SmartPLS is a software application for (graphical) path modeling with latent variables (LVP). The partial least squares (PLS)-method is used for the LVP-analysis in this software.
What is path analysis?
What are general assumptions?
What is input path diagram?
What is output path diagram?
How unexplained variance is shown in path diagram?
Statswork- Lecture:1: Structural Equation Modeling (SEM) using AMOS (www.stat...Stats Statswork
Statswork (Statswork.com), Lecture:1 – Introductory Videos of Structural Equation Modeling (SEM) using AMOS, SEM and what are the values extracted from the Test?
Structural Equation Modeling (SEM) is an extension of the general linear model. It is used to test a set of regression equations simultaneously. SEM represents the relationship between dependent (unobserved) variable and independent (observed) variables using path diagrams. Watch more.
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
Estimators for structural equation models of Likert scale dataNick Stauner
Which estimation method is optimal for structural equation modeling (SEM) of Likert scale data? Conventional SEM assumes continuous measurement, and some SEM estimators assume a multivariate normal distribution, but Likert scale data are ordinal and do not necessarily resemble a discretized normal distribution. When treated as continuous, these data may yet be skewed due to item difficulty, choice of population, or various response biases. One can fit an SEM to a matrix of polychoric correlations, which estimate latent, continuous constructs underlying ordinally measured variables, but polychoric correlations also assume these latent factors are normally distributed. To what extent are these methods robust with continuous versus ordinal data and with varying degrees of skewness and kurtosis? To answer, I simulated 10,000 samples of multivariate normal data, each consisting of 500 observations of five strongly correlated variables. I transformed each consecutive sample to an incrementally greater degree to increase skew and kurtosis from approximately normal levels to extremes beyond six and 30, respectively. I then performed five confirmatory factor analyses on each sample using five different estimators: maximum likelihood (ML), weighted least squares (WLS), diagonally weighted least squares (DWLS), unweighted least squares (ULS), and generalized least squares (GLS). I compared results for continuous and discretized (ordinal) data, including loadings, error variances, fit statistics, and standard errors. I also noted frequencies of failures, which complicated calculation of polychoric correlations, and particularly plagued the WLS estimator. WLS estimation produced relatively biased loadings and error variance estimates. GLS also underestimated error variances. Neither estimator exhibited any unique advantage to offset these disadvantages. ML estimated parameters more accurately, but some fit statistics appeared biased by it, especially in the context of extreme nonnormality. Specifically, the chi squared goodness-of-fit test statistic and the root mean square error of approximation (RMSEA) began higher with ML-estimated SEMs of approximately normal data, and worsened sharply with greater nonnormality. The Tucker Lewis Index (TLI) and standardized root mean square residual (SRMR) also worsened more moderately with nonnormality when using ML estimation. GLS-estimated fit statistics shared ML’s sensitivity to nonnormality, and were even worse for the TLI and SRMR. Results generally favored ULS and DWLS estimators, which produced accurate parameter estimates, good and robust fit statistics, and small standard errors (SEs) for loadings. DWLS tended to produce smaller SEs than ULS when skewness was below three, but ULS SEs were more robust to nonnormality and smaller with extremely nonnormal data. ML SEs were larger for loadings, but smaller for error variance estimates, and fairly robust to nonnormality...
SmartPLS is a software application for (graphical) path modeling with latent variables (LVP). The partial least squares (PLS)-method is used for the LVP-analysis in this software.
Research with Partial Least Square (PLS) based Structural Equation Modelling ...Tuhin AI Advisory
A STRUCTURAL MODELING APPROACH TO COMPREHEND PURCHASE INTENTION INFLUENCED BY SOCIAL MEDIA : THE MEDIATING ROLE OF CONSUMER ATTITUDE AND THE MODERATING ROLE OF MARKET MAVENS
proposal dibentangkan di ICABEC 2011, Primula Hotel, Kuala Terengganu, 2 November 2011. Sebahagian daripada projek FRGS bersama student/member Alif Bakar.
Implementation of SEM Partial Least Square in Analyzing the UTAUT ModelAJHSSR Journal
ABSTRACT:Partial Least Squares (PLS) Structural Equation Modeling (PLS-SEM) is a statistical technique
used to analyze the expected connections between constructs by evaluating the existence of correlations or
impacts among these constructs. The objective of this work is to employ the Structural Equation Modeling
(SEM) technique, specifically Partial Least Squares (PLS), to investigate the Unified Theory of Acceptance and
Use of Technology (UTAUT) model in the specific domain of payment technology acceptance and utilization.
The UTAUT model encompasses latent variables classified into independent, mediator, moderator, and
dependent categories. Hence, the appropriate approach, the partial least squares structural equation modeling
(PLS-SEM) method, was chosen. The rationale behind this decision is the capability of PLS-SEM to assess
models with a relatively limited dataset, as demonstrated in this study, which included a sample of 50
participants. This study employs a quantitative methodology utilizing a survey-based approach to gather data via
questionnaires. The UTAUT model in the technology acceptance and use domain was accurately assessed by
PLS-SEM, as evidenced by the findings. The findings have substantial implications for comprehending the
factors that influence the adoption of payment technology, specifically focusing on the linkages between
constructs in the UTAUT model. This research validates the model and establishes a foundation for a more
profound comprehension of user behavior in accepting and utilizing payment technologies. Ultimately, using
PLS-SEM demonstrated its efficacy in examining the UTAUT model.
KEYWORDS :Structural Equation Model, Partial Least Square, UTAUT
International Journal of Mathematics and Statistics Invention (IJMSI) inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Adaptive guidance model based similarity for software process development pro...ijseajournal
This paper describes a modeling approach SAGM (Similarity for Adaptive Guidance Model) that provides
adaptive and recursive guidance for software process development. This approach, in accordance to
developer needs, allows specific tailored guidance regarding the profile of developers. A profile is partially
or completely defined from a model of developers, through their roles, their qualifications, and through the
relationships between the context of the current activity and the model of the defined activities. This
approach aims to define the generic profile of development context and a similarity measure that evaluates
the similarities between the profiles created from the model of developers and those of the development
team involved in the execution of a software process. This is to identify the profiles classification and to
deduce the appropriate type of assistance to developers (that can be corrective, constructive or specific).
FORMALIZATION & DATA ABSTRACTION DURING USE CASE MODELING IN OBJECT ORIENTED ...cscpconf
In object oriented analysis and design, use cases represent the things of value that the system performs for its actors in UML and unified process. Use cases are not functions or features.
They allow us to get behavioral abstraction of the system to be. The purpose of the behavioral abstraction is to get to the heart of what a system must do, we must first focus on who (or what)
will use it, or be used by it. After we do this, we look at what the system must do for those users in order to do something useful. That is what exactly we expect from the use cases as the
behavioral abstraction. Apart from this fact use cases are the poor candidates for the data abstraction. Rather the do not have data abstraction. The main reason is it shows or describes
the sequence of events or actions performed by the actor or use case, it does not take data in to account. As we know in earlier stages of the development we believe in ‘what’ rather than
‘how’. ‘What’ does not need to include data whereas ‘how’ depicts the data. As use case moves around ‘what’ only we are not able to extract the data. So in order to incorporate data in use cases one must feel the need of data at the initial stages of the development. We have developed the technique to integrate data in to the uses cases. This paper is regarding our investigations to take care of data during early stages of the software development. The collected abstraction of data helps in the analysis and then assist in forming the attributes of the candidate classes. This makes sure that we will not miss any attribute that is required in the abstracted behavior using use cases. Formalization adds to the accuracy of the data abstraction. We have investigated object constraint language to perform better data abstraction during analysis & design in unified paradigm. In this paper we have presented our research regarding early stage data abstraction and its formalization.
Formalization & data abstraction during use case modeling in object oriented ...csandit
In object oriented analysis and design, use cases represent the things of value that the system
performs for its actors in UML and unified process. Use cases are not functions or features.
They allow us to get behavioral abstraction of the system to be. The purpose of the behavioral
abstraction is to get to the heart of what a system must do, we must first focus on who (or what)
will use it, or be used by it. After we do this, we look at what the system must do for those users
in order to do something useful. That is what exactly we expect from the use cases as the
behavioral abstraction. Apart from this fact use cases are the poor candidates for the data
abstraction. Rather the do not have data abstraction. The main reason is it shows or describes
the sequence of events or actions performed by the actor or use case, it does not take data in to
account. As we know in earlier stages of the development we believe in ‘what’ rather than
‘how’. ‘What’ does not need to include data whereas ‘how’ depicts the data. As use case moves
around ‘what’ only we are not able to extract the data. So in order to incorporate data in use
cases one must feel the need of data at the initial stages of the development. We have developed
the technique to integrate data in to the uses cases. This paper is regarding our investigations
to take care of data during early stages of the software development. The collected abstraction
of data helps in the analysis and then assist in forming the attributes of the candidate classes.
This makes sure that we will not miss any attribute that is required in the abstracted behavior
using use cases. Formalization adds to the accuracy of the data abstraction. We have
investigated object constraint language to perform better data abstraction during analysis &
design in unified paradigm. In this paper we have presented our research regarding early stage
data abstraction and its formalization.
Modeling and simulation is the use of models as a basis for simulations to develop data utilized for managerial or technical decision making. In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model.
A SIMILARITY MEASURE FOR CATEGORIZING THE DEVELOPERS PROFILE IN A SOFTWARE PR...csandit
Software development processes need to have an integrated environment that fulfills specific
developer needs. In this context, this paper describes the modeling approach SAGM ((Similarity
for Adaptive Guidance Model) that provides adaptive recursive guidance for software
processes, and specifically tailored regarding the profile of developers. A profile is defined from
a model of developers, through their roles, their qualifications, and through the relationships
between the context of the current activity and the model of the activities. This approach
presents a similarity measure that evaluates the similarities between the profiles created from
the model of developers and those of the development team involved in the execution of a
software process. This is to identify the profiles classification and to deduce the appropriate
type of assistance (that can be corrective, constructive or specific) to developers.
A brief introduction to network simulation and the difference between simulator and emulator along with the most important types of simulations techniques.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Instructions for Submissions thorugh G- Classroom.pptx
Bengkel smartPLS 2011
1. Introduction to SmartPLS By: Azwadi Ali Department of Accounting and Finance, Faculty of Management and Economics, Universiti Malaysia Terengganu. FPE, UMT. 23 December 2010.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12. Reflective vs Formative Example: Computer Self-Efficacy Reflective – I am capable at performing tasks on my computer. I feel confident in my ability to perform computer-related tasks. Formative – I am confident at my ability to perform tasks in MS Word. I am skillful at using Excel. Example: System Quality Reflective – Overall, I would rate the system quality of the system highly. The quality of the system is appropriate for my needs. Formative – Reliability, Ease of Use, Complexity, Accessibility, Responsiveness
17. Research hypotheses H1: ‘Information usefulness’ is positively related to ‘attitude towards IR Websites’ H2: ‘Usability’ is positively related to ‘attitude towards IR Websites’ H3: ‘Attractiveness’ is positively related to ‘attitude towards IR Websites’ H4: ‘Attitude towards IR Websites’ is positively related to ‘intention to re-use IR Website’